Evaluation of Different Dose-Response Models for High Hydrostatic Pressure Inactivation of Microorganisms

Abstract

Modeling of microbial inactivation by high hydrostatic pressure (HHP) requires a plot of the log microbial count or survival ratio versus time data under a constant pressure and temperature. However, at low pressure and temperature values, very long holding times are needed to obtain measurable inactivation. Since the time has a significant effect on the cost of HHP processing it may be reasonable to fix the time at an appropriate value and quantify the inactivation with respect to pressure. Such a plot is called dose-response curve and it may be more beneficial than the traditional inactivation modeling since short holding times with different pressure values can be selected and used for the modeling of HHP inactivation. For this purpose, 49 dose-response curves (with at least 4 log10 reduction and ≥5 data points including the atmospheric pressure value (P = 0.1 MPa), and with holding time ≤10 min) for HHP inactivation of microorganisms obtained from published studies were fitted with four different models, namely the Discrete model, Shoulder model, Fermi equation, and Weibull model, and the pressure value needed for 5 log10 (P5) inactivation was calculated for all the models above. The Shoulder model and Fermi equation produced exactly the same parameter and P5 values, while the Discrete model produced similar or sometimes the exact same parameter values as the Fermi equation. The Weibull model produced the worst fit (had the lowest adjusted determination coefficient (R2adj) and highest mean square error (MSE) values), while the Fermi equation had the best fit (the highest R2adj and lowest MSE values). Parameters of the models and also P5 values of each model can be useful for the further experimental design of HHP processing and also for the comparison of the pressure resistance of different microorganisms. Further experiments can be done to verify the P5 values at given conditions. The procedure given in this study can also be extended for enzyme inactivation by HHP.

Microbial Dose-Response Curves and Disinfection Efficacy Models Revisited

“…A prominent flat shoulder in a type II dose-response curves (of the kind shown in Fig. 2 (right)) has been reported in various publications (e.g., [8,32]). The shoulder's presence has been commonly considered a special case where the drop in the survival ratio commences only after the administration of a critical dose or as a consequence of a characteristic physiological lag time.…”

Pulsed Light (PL) Treatments on Almond Kernels: Salmonella enteritidis Inactivation Kinetics and Infrared Thermography Insights

“…Input power conditions of 1500 W (A, D, and G), 1250 W (B, E, and H) and 1000 W (C, F, I). Gray lines indicate extrapolated interval from the application of non-traditional agents, such as pulsed light (Hwang et al, 2019;Izquier & Gómez-López, 2011), high-pressure processing (HPP) (Buzrul, 2017;Yuan et al, 2016), and pulsed electric field (Soliva-Fortuny et al, 2006). Their goodness-of-fit is evaluated statically (see "Kinetics of Microbial Inactivation and Tested Models for PL Treatments" section).…”

Is It Possible to Make Fewer Experiments: Prediction of Bacterial Survival/Death Probability for High-Pressure Processing With the Bayesian Approach?

“…The changes caused by these variables should be understood, controlled and optimised carefully to ensure the safety of the final product. For that purpose, several statistical learning methods such as kinetic models (Buzrul, 2014;Serment-Moreno et al, 2014), neural networks (Hajmeer and Basheer, 2002), Weibull model (Buzrul et al, 2008), logistic regression (Buzrul, 2019;Koseki et al, 2009;Koseki and Yamamoto, 2007;Wang et al, 2017), piecewise models (Buzrul, 2017) have been utilised. Among these modelling approaches, logistic regression (LR), has been widely accepted by researchers due to its ability to handle nonlinear kinetics of microbial inactivation and ease of implementation (Koseki et al, 2009).…”